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Image Segmentation Using an Evolutionary Method Based on Allostatic Mechanisms

  • Valentín Osuna-Enciso
  • Virgilio Zúñiga
  • Diego Oliva
  • Erik Cuevas
  • Humberto Sossa
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 630)

Abstract

In image analysis, segmentation is considered one of the most important steps. Segmentation by searching threshold values assumes that objects in a digital image can be modeled through distinct gray level distributions. In this chapter it is proposed the use of a bio-inspired algorithm, called Allostatic Optimisation (AO), to solve the multi threshold segmentation problem. Our approach considers that an histogram can be approximated by a mixture of Cauchy functions, whose parameters are evolved by AO. The contributions of this chapter are on three fronts, by using: a Cauchy mixture to model the original histogram of digital images, the Hellinger distance as an objective function, and AO algorithm. In order to illustrate the proficiency and robustness of the proposed approach, it has been compared to the well-known Otsu method, over several standard benchmark images.

Keywords

Hausdorff Distance Cauchy Distribution Hellinger Distance Matching Quality Combination Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Valentín Osuna-Enciso
    • 1
  • Virgilio Zúñiga
    • 1
  • Diego Oliva
    • 2
  • Erik Cuevas
    • 3
  • Humberto Sossa
    • 4
  1. 1.Departamento de IngenieríasCUTonalá, Universidad de GuadalajaraTonaláMexico
  2. 2.Departamento de Ciencias ComputacionalesTecnológico de Monterrey, Campus GuadalajaraGuadalajaraMexico
  3. 3.Departamento de Ciencias ComputacionalesCUCEI, Universidad de GuadalajaraGuadalajaraMexico
  4. 4.Instituto Politécnico Nacional-CICMéxico D.F.Mexico

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